Bootstrapping Relevance: Making Web Conversions Meaningful for Long Sales Cycles

Most hurricanes that reach the United States start off the coast of West Africa. Those storms join and split with other minor systems as they move across the Atlantic. Some dissipate into a mild breeze; others devastate coastal areas along the Eastern seaboard.

So what does an afternoon rainshower over Cape Verde tell you about the next Category 5 hurricane? Often, little more than a form fill tells you about the potential for a five-figure sale months down the road.

Google Analytics insights frequently end with raw counts of goal completions, leaving a yawning gap between on-site behavior and sales for companies with long sales cycles.

More challenging still, the space between marketers’ realities and solutions is equally vast: Seamless integration of marketing and sales data or a Google Analytics 360 subscription is aspirational.

This post details four steps that any organization can follow to estimate the value of on-site conversions more accurately:

Identify every potential touchpoint.Organize existing data into an idealized customer journey.Integrate data into goal completions.Analyze and act on that data.

No solution is perfect, but incremental progress is possible—and worthwhile.

Why bother? Analytics incentivize behavior

The data-related challenges of long sales cycles are well known: Between a form fill and a sale, there may be dozens of touchpoints spanning weeks or months. Those interactions occur across teams (marketing, sales, customer support) and platforms (analytics, CRM, email).

The challenge of joining those datasets resigns many marketers to limited measurement: We know our data is incomplete, so we might as well just count form fills.

A common focus for companies with long sales cycles is attribution. But even data-driven attribution, robust as it may be, usually improves attribution of form fills or PDF downloads—marketing metrics that may be weak indicators of sales.

Goal completions can become stronger predictors of sales by pushing data about the relative value of each goal completion back into analytics.

Attribution’s relevance depends on the known value of the conversion.

Regardless of how much data you have, you will make decisions on how to allocate marketing resources. Partial data—or even anecdotal data—can, at the bare minimum, form the basis for experimentation and a means to test your assumptions.

It starts with a survey of all known customer data.

Step 1: Identify every potential touchpoint.

“Long lead time before the sale is an opportunity to do more data collection,” offered Snowplow Analytics’ Anthony Mandelli, “which will ultimately help you in the long run.”

Compare the number of touchpoints in a year-long sales process to the purchase of novelty socks (Mandelli’s example). The latter is a single image, the former a feature-film—a complete narrative with deep insight into what influences consumer behavior.

“It’s a long sales cycle for a reason,” Mandelli continued. “Leads are conducting online and offline research.” The starting point, then, is to “get all your data together somewhere—start with the first interaction, then all the way to purchase.”

It may also include reports from your sales team, estimates by executives, or other offline sources. At the outset, you simply want to know all the potential sources of data (regardless of whether you’re able to gather them into a Customer Data Platform that curates “a single source of truth“).

You may be missing key data or may not be able to integrate it in future steps, but knowing what exists—and what is or isn’t accessible—helps establish the immediate path forward and guides future improvements.

Step 2: Organize existing data into an idealized customer journey.

Sketching an idealized user journey—or reviewing one already created—is not about forcing users into a linear funnel but about creating a structure to help organize your data.

A customer journey map, Hull’s Ed Fry explains, “highlights the macro-conversions that many teams in the company optimize for (like a new user signing up) vs. micro-conversions that concern few other people.” Each stage in the journey, in turn, is delineated by a conversion:

In a customer journey, the step-by-step progress of a user usually includes a measurable conversion in a digital channel. (Image source)

In an example Mandelli shared, a flooring company had no visibility into what happened between a potential buyer’s $10 sample purchase and a $10,000 sale. Building an idealized user journey—based on data from a real customer—helped the company organize the data they had by the steps the customer took:

Web ad (Google AdWords or Bing)Visit the websiteOrder a sample from the websiteReview samplesReceive drip email marketing campaignPurchase flooring (through the web or on the phone

With existing data points plotted along the idealized user journey, ask yourself: “Where are the biggest gaps between touches?” (In the above example, it’s Step 4.) “The goal is not to sink under analysis paralysis,” writes Fry. “It is to simply understand the backbone of your customer journeys.”

A data gap does not invalidate conversion values for long sales cycles. Charles Farina of Analytics Pros explained:

If you are able to qualify a lead quickly, work to connect your metrics to center on qualified leads. From there, try and work further down the funnel.

In other words, if a form fill can be qualified with a second interaction (say, responding to a phone call), that data—the percentage of form fills who become qualified leads—can guide conversion valuation, even if months pass before those qualified leads become sales.

Even with complete data, Farina suggested, you’ll rarely optimize based on close-of-sale metrics: It simply takes too long. If you make changes to service pages today, would you put everything on hold for months while you waited to see how many leads from the updated pages became customers?

Focus on bringing more quality into your funnel, then use the fully connected journey to make additional optimizations on top.

For many, the perspective is liberating: Data points from one or two steps post–form fill can make conversion data vastly more relevant, no matter how long the sales cycle stretches past the initial conversion.

Step 3: Integrate data into goal completions.

There are elegant solutions for integrating Analytics data with CRM data and similar sources:

The potential value of an integration—like pulling Salesforce data into Google Analytics—is clear, but securing the budget is, for most, unrealistic. (Image source)

In the prior example of the flooring company, Snowplow joined the data from web analytics and marketing automation tools to provide ongoing visibility about how users progressed through the journey. But that ongoing portrait—while closer to the ideal—isn’t mandatory.

If you don’t have a sizeable analytics budget or an in-house team of developers to manage multiple connections, use a snapshot of your post-conversion data to adjust Goal Values in Google Analytics.

To set Goal Values, you need to calculate the value of a lead on a goal-by-goal basis. In its simplest form, the process divides the total number of goal completions by the revenue from those conversions.

100 form fills5 form fills convert to salesEach sale generates $10,000 in revenue

Thus, a form fill is worth $500. The calculation requires two data points outside Google Analytics: The number of web leads who became customers, and the value of each sale. (If you don’t have access to both, skip to the second option.)

In a perfect world, the calculations are exact enough to establish ROI for marketing efforts. However, for long sales cycles, obtaining that degree of accuracy is almost impossible—but that shouldn’t keep you from using Goal Values.

Goal Values Are fixed numbers…with relative value

When it comes to long sales cycles, setting the Goal Value of a form fill is less about ROI and more about weighting the impact of on-site behavior. Relative differences in dollar values, as detailed in the fourth step, allow for better comparisons of how each page or channel performs.

For example, if a lead who initiates an engagement with a phone call—tracked via CallRail or Marchex—closes at twice the rate of a form fill, that difference will be reflected in the Goal Value. Likewise, a newsletter signup from a blog post will probably be weighted less (by using sales data from newsletter subscribers).

To think of it another way, not assigning Goal Values gives every goal the same value: $0. If your Goal Values aren’t accurate enough to determine ROI—whether left as $0 or calculated based on sales data—you might as go with the calculated estimate that at least has a chance of being directional.

Note: If seeing “inaccurate” Goal Value figures will ruffle feathers in other departments, create a new View with the same Goals and add estimated Goal Values.

Use Lookup Tables to generate dynamic Goal Values

Not all form fillers—even of the same form—are equal. A Lookup Table in Google Tag Manager (GTM), as Bounteous details, can set dynamic Goal Values based on form inputs.

So, for example, if a form question includes the size of the company, you can adjust the Goal Value based on the likelihood of conversion, average order value, or lifetime value of that demographic.

Set a different Output (Goal Value) for each based on Input (the form-field options):

The Default Value is used if none of the other criteria is met.

Create a Data Layer variable to capture the business category data (the Input field) upon submission. Then, create an Event that pulls in the business category information and the associated lead value from the Lookup Table.

Finally, use the Event value as the Goal Value for the that conversion:

Even if you don’t know the value of a given type of lead—or any lead at all—you still have another option.

2. Estimate the relative value of online touchpoints

If quantitative data on lead conversion rates and order value isn’t available, you can add relative values. Branko Kral of Orbit Media detailed the process for a stem-cell clinic with a long sales cycle and limited data.

They identified the primary touchpoints, then assigned relative values from $100 to $10—the actual dollar values were irrelevant—to gauge the impact of campaigns that spurred a range of micro- and macro-conversions:

First-time calls – lead to most new businessRepeating calls – also highly valuableCall-back requests – capture contact info and explicitly ask to be contactedBlog subscriptions – capture contact info and indicate trustVideo views > 50% of the video length – patients who book often mention they’ve watched the patient testimonial videosEmail link clicks – typical for inquiries higher up the funnelSocial share clicks – spread the wordViews of a Contact Us page – a subtle but valuable indicator of interest

It’s easy to poke holes in the process: How do you know that a social share click is worth say, half that of a video view? You don’t. However, that initial, heuristic estimate is a baseline for hypothesis development and testing.

After all, if you don’t assign Goal Values, you’re still allocating resources based on which actions you perceive to be most valuable. Adding relative Goal Values to on-site conversions makes it easy to visualize the implications of your assumptions throughout your site.

In Google Analytics, Page Value “is the average value for a page that a user visited before landing on the goal page or completing an Ecommerce transaction (or both).” As Effin Amazing notes:

Goals are a Session dimension metric, which means that you cannot use them in a Hit dimension report like Pages report, Event reports, or any type of Custom report built around a Hit dimension.

Page Value bridges the gap between these Session dimensions and Hit dimensions by tying a specific page URL to a monetary value when users complete a goal or transaction.

It’s one way to see the value of content at a URL level. With a Goal Value calculated from actual sales data, the Page Value metric may (roughly) estimate revenue; without it, it still offers a weighted estimate of importance for pages in the conversion process.

That URL-by-URL view can break down further into:

Mediums (e.g. organic vs. direct visits to the same page or group of pages)Website sections (e.g. /case-studies/ vs. /whitepapers/)Anything else you can think to add as a secondary dimension.

A caveat on taking action

A one-time estimate of close rates or average order value is good for only so long. The more often (monthly, quarterly) those calculations can be reworked—and Goal Values adjusted—the more reliable that data will be. (Goal Values are not assigned retroactively.)

Further, if an initial estimate suggests that email visitors are more lucrative than those from other channels, that may justify a push to acquire more email addresses—only to capture the addresses of less-relevant, less ready-to-buy visitors.

Every update of your Goal Values, then, is an opportunity to spot diminishing returns and shift marketing resources to another channel or site section. Disappointing as it may be to realize that you’ve exhausted a strategy, you’ll never notice unless you rerun the numbers—all you’ll see is conversions trending up, a vanity metric reaching ever-higher to nowhere.

Conclusion

When it comes to long sales cycles and web conversions, “perfect” is often the enemy of anything. But just because you don’t have uninterrupted lead-to-sale data doesn’t mean you can’t make your web analytics more meaningful.

Indeed, the second and third interactions after an on-site conversion—those you’re most likely to have on hand—may be the most influential metrics no matter how much data you accumulate.

Importing calculated Goal Values based on those metrics back into Google Analytics offers a more accurate valuation of the actions that take place on your website.

Even if those values are relative, you gain visibility into the assumptions you have about your site. Whether or not they hold true, the outcome will improve your marketing.